Scene Recognition Mechanism for Service Robot Adapting Various Families: A CNN-based Approach Using Multi-Type Cameras

The key challenges of scene recognition for service robots in various family environments are the view shortage of holistic scenes and poor adaptation. To address these problems, a family scene recognition mechanism for the service robot is proposed in this paper. A comprehensive application of fish-eye, pinhole, and depth cameras is provided to guarantee the sufficient view of robot. A selective CNN features fusion for the recognition of fish-eye scene images is designed to improve the training efficiency and the recognition accuracy. The mechanism is deployed in a designed hybrid cloud including public and private clouds. The proposed family scene recognition model is trained by large-scale datasets in the public cloud and runs in the private cloud. Besides, the recognition skill can be reinforced and increased by matching human guidance and CNN features to help the robot learn new scenes and improve the adaptation in different family environments. Extensive experiments are implemented to evaluate the proposed method using real scene images from six families. The experiment results show the validity and good performance of our method for the service robot scene recognition in various family environments.